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AI-Powered One Health Risk Indicator Search and Compilation

Environmental Health · Research & Development

What it collects

Operational data
Anonymized data
  • Internal AAFC SharePoint site content including operational documents and existing data holdings used as part of the search and compilation workflow.
Generated content
Anonymized data
  • Publicly available research papers and website content on zoonotic diseases, antimicrobial resistance, transmissible animal diseases, and plant and animal diseases ingested as input for search and compilation.
Run by
Agriculture and Agri-Food Canada (AAFC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization

What it is for

Environmental HealthResearch & Development

This tool helps Government of Canada employees search, collect, and compile publicly available One Health risk indicators — including data on zoonotic diseases, antimicrobial resistance, and plant and animal diseases. It uses large language model (LLM) technology to automate the gathering and structuring of information from research papers and public websites. The system does not process personal information, and users are informed when AI is in use.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Internal AAFC SharePoint site content including operational documents and existing data holdings used as part of the search and compilation workflow.
Generated content
Anonymized data
  • Publicly available research papers and website content on zoonotic diseases, antimicrobial resistance, transmissible animal diseases, and plant and animal diseases ingested as input for search and compilation.

Processing

Language Models
  • Large language model (LLM) technology is used to search, filter, and synthesize publicly available One Health risk indicator data from diverse sources including research papers and websites.
Optimization
  • Workflow automation and process optimization components handle deduplication of sources and structured export of outputs to build a cumulative, non-redundant repository.

What it does

Creating (Generative AI)
Human decides
  • LLM-based search generates structured summaries and compiled outputs from publicly available research papers and websites; GC employee users review and act on the results.
Acting (Agentic AI)
Human executes
  • Process optimization and workflow automation capabilities handle the filtering, deduplication, and structured export of compiled data sources; human staff direct and review the workflow.

Outputs

Operational data
Anonymized data
  • Structured, editable compilations of One Health risk indicators exported from the system; stored in a cumulative repository for use by GC employees in analysis and decision support.

Run by

Agriculture and Agri-Food Canada (AAFC)
  • Federal department responsible for deploying the OneHealth Emergency Tool to support GC employees in compiling publicly available One Health risk indicators.

GC AI Register — 2526-AAFC-AAC-011

Built by

Government of Canada
  • The Government of Canada developed this AI tool internally, as recorded in the official AI register.

GC AI Register — 2526-AAFC-AAC-011

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Outputs are available to GC employees, the primary users of the system, within Agriculture and Agri-Food Canada.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Be Informed of AI UseGC employee users are informed when AI is in use, as confirmed in the register (AI use disclosed to users: Y). No personal information about the public is processed by this system.
  • Right to Algorithmic TransparencyThe system is listed on the Government of Canada's public AI register, providing transparency about its purpose, capabilities, and data sources to any interested party. Details are available at the register URL.

Risks and safeguards

  • Societal & cultural harmLLM-generated summaries of publicly available research may introduce errors, omissions, or outdated information into the One Health risk indicator repository, potentially affecting downstream policy or research decisions.Safeguard: GC employees review outputs before use; the system is in development with ongoing refinement; outputs are structured and editable to allow correction.